Landscape of multi-tissue global gene expression reveals the regulatory signatures of feed efficiency in beef cattle
Bibliographic record
Abstract
MOTIVATION: Feed efficiency is an important trait for sustainable beef production that is regulated by the complex biological process, but the mode of action behinds it has not been clearly defined. Here, we aimed to elucidate the regulatory mechanisms of this trait through studying the landscape of the genome-wide gene expression of rumen, liver, muscle and backfat tissues, the key ones involved in the energy metabolism. RESULTS: The transcriptome of 189 samples across four tissues from 48 beef steers with varied feed efficiency were generated using Illumina HiSeq4000. The analysis of global gene expression profiles of four tissues, functional analysis of tissue-shared and -unique genes, co-expressed network construction of tissue-shared genes, weighted correlations analysis between gene modules and feed efficiency-related traits in each tissue were performed. Among four tissues, the transcriptome of muscle tissue was distinctive from others, while those of rumen and backfat tissues were similar. The associations between co-expressed genes and feed efficiency related traits at single or all tissues level exhibited that the gene expression in the rumen, liver, muscle and backfat were the most correlated with feed conversion ratio, dry matter intake, average daily gain and residual feed intake, respectively. The 19 overlapped genes identified from the strongest module-trait relationships in four tissues are potential generic gene markers for feed efficiency. AVAILABILITY AND IMPLEMENTATION: The distribution of gene expression data can be accessed at https://www.cattleomics.com/transcriptome. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".